Dan Schlauch
March 7, 2016
The following plots demonstrate that commonly used NI methods to not produce reproducible results.
Edgeweight differences for COPD and Smoker Controls were taken across studies.
Edgeweight differences are essentially uncorrelated across studies.
This holds for all tested NI methods.
Network differences between cases and controls are completely uncorrelated between COPD studies using commonly the used WGCNA, CLR, and ARACNE. (Far less than 1% of the variation in one study is explained by the variation in any other)
Maybe not. Is there any way to observe drivers of state transitions at the network level using this information?
Yes! Our [NAME] method focuses on regulatory patterns rather than individual edges and DOES validate across studies. This is our “story”.
Name suggestion:
MOdeling Network State Transitions from Expression and Regulatory data
(MONSTER)
JQ Comments: Figure for transition heatmap
Figure 1.
JQ comments:
“the matrices should represent the networks”
vs
\[ B=AT \] is now \[ B=A\Psi \]
Do we want/need this? Can table replace it? Or keep table in supplement (it's big).
More supporting literature validation
Autophagy in chronic obstructive pulmonary disease: Homeostatic or pathogenic mechanism? Ryter (2008 and 2010), others
Egr-1 Regulates Autophagy in Cigarette Smoke-Induced Chronic Obstructive Pulmonary Disease Chen et al. (2008)
ELK1, ELK4 implicated in asthma.
Emerging role of MAP kinase pathways as therapeutic targets in COPD Mercer (2006) ELK1 phosphorylated by ERK1/2